Data assimilation with C-IFS

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Atmos. Chem. Phys. Discuss., 15, 4265–4331, 2015 www.atmos-chem-phys-discuss.net/15/4265/2015/ doi:10.5194/acpd-15-4265-2015 © Author(s) 2015. CC Attribution 3.0 License.

This discussion paper is/has been under review for the journal Atmospheric Chemistry and Physics (ACP). Please refer to the corresponding final paper in ACP if available.

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ECMWF, Reading, UK Institute of Environmental Physics, University of Bremen, Germany 3 Max Planck Institute for Meteorology, Hamburg, Germany 4 Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium 5 Royal Netherlands Meteorological Institute, De Bilt, the Netherlands 6 Université de Toulouse, UPS, LA (Laboratoire d’Aérologie), Toulouse, France 7 CNRS, LA (Laboratoire d’Aérologie), UMR 5560, 31400 Toulouse, France 8 Academy of Athens, Athens, Greece 2

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Data assimilation with C-IFS A. Inness et al.

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A. Inness , A.-M. Blechschmidt , I. Bouarar , S. Chabrillat , M. Crepulja , R. J. Engelen1 , H. Eskes5 , J. Flemming1 , A. Gaudel6,7 , F. Hendrick4 , V. Huijnen5 , L. Jones1 , J. Kapsomenakis8 , E. Katragkou9 , A. Keppens4 , B. Langerock4 , 4 10 1 1 1 M. de Mazière , D. Melas , M. Parrington , V. H. Peuch , M. Razinger , 2 11 1 6,7 8,12 A. Richter , M. G. Schultz , M. Suttie , V. Thouret , M. Vrekoussis , 13 8 A. Wagner , and C. Zerefos

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Data assimilation of satellite retrieved ozone, carbon monoxide and nitrogen dioxide with ECMWF’s Composition-IFS

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Department of Meteorology and Climatology, School of Geology, Aristotle University of Thessaloniki, Greece 10 Laboratory of Atmospheric Physics, School of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece 11 Forschungszentrum Jülich, Jülich, Germany 12 EEWRC, The Cyprus Institute, Cyprus 13 Deutscher Wetterdienst, Meteorologisches Observatorium Hohenpeissenberg, Germany

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Correspondence to: A. Inness ([email protected]) Published by Copernicus Publications on behalf of the European Geosciences Union.

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Received: 22 December 2014 – Accepted: 29 January 2015 – Published: 17 February 2015

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Daily global analyses and 5 day forecasts are generated in the context of the European Monitoring Atmospheric Composition and Climate (MACC) project using an extended version of the Integrated Forecasting System (IFS) of the European Centre for MediumRange Weather Forecasts (ECMWF). IFS now includes modules for chemistry, deposition and emission of reactive gases, aerosols, and greenhouse gases, and the 4-dimensional variational data assimilation scheme makes use of multiple satellite observations of atmospheric composition in addition to meteorological observations. This paper describes the data assimilation setup of the new Composition-IFS (C-IFS) with respect to reactive gases and validates analysis fields of ozone (O3 ), carbon monoxide (CO), and nitrogen dioxide (NO2 ) for the year 2008 against independent observations and a control run without data assimilation. The largest improvement in CO by assimilation of MOPITT CO columns is seen in the lower troposphere of the Northern Hemisphere (NH) Extratropics during winter, and during the South African biomass burning season. The assimilation of several O3 total column and stratospheric profile retrievals greatly improves the total column, stratospheric and upper tropospheric O3 analysis fields relative to the control run. The impact on lower tropospheric ozone, which comes from the residual of the total column and stratospheric profile O3 data, is smaller, but nevertheless there is some improvement particularly in the NH during winter and spring. The impact of the assimilation of OMI tropospheric NO2 columns is small because of the short lifetime of NO2 , suggesting that NO2 observations would be better used to adjust emissions instead of initial conditions. The results further indicate that the quality of the tropospheric analyses and of the stratospheric ozone analysis obtained with the C-IFS system has improved compared to the previous “coupled” model system of MACC.

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Air pollution has become the biggest environmental health risk killing about 7 million people in 2012 according to a recent WHO study (WHO 2014). It is therefore important to provide air quality forecasts on global, regional and local scales to enable vulnerable people to take preventative action during pollution episodes. The Monitoring Atmospheric Composition and Climate (MACC) project (www.copernicus-atmosphere.eu) is the pre-operational atmospheric service of the European Copernicus programme funded by the European Commission’s framework program 7 (FP7). MACC will evolve into the Copernicus Atmospheric Monitoring Service in 2015. MACC combines stateof-the art chemistry and transport models with satellite data from various sensors to provide consistent global analyses and forecasts of 3-dimensional fields of the atmospheric composition, including ozone (O3 ), carbon monoxide (CO), nitrogen dioxide (NO2 ), sulphur dioxide (SO2 ), formaldehyde (HCHO), as well as methane (CH4 ), carbon dioxide (CO2 ) and aerosols (Flemming et al., 2013). The MACC-system is run routinely in near-real time (NRT) and provides daily 5 day forecasts of tropospheric and stratospheric composition at a horizontal resolution of about 80 km globally. For details of the system configuration, see http://www.copernicus-atmosphere.eu/oper_info/. An earlier version of the system was also used to produce a 10 year reanalysis of atmospheric composition data covering the years 2003 to 2012 (Inness et al., 2013). To improve the quality of the MACC forecasts the initial conditions for some of the chemical species are provided by data assimilation of atmospheric composition observations from satellites (Benedetti et al., 2009; Inness et al., 2013; Massart et al., 2014). The use of data assimilation for atmospheric composition goes back almost two decades (Fisher and Lary, 1995; Elbern et al., 1997; Elbern and Schmidt, 1999, 2001; Lamarque et al., 1999; Khattatov et al., 2000; Ménard et al., 2000; Errera and Fonteyn, 2001). The overview articles by Carmichael et al. (2007) and Sandu and Chai (2011) describe the various approaches used for chemical data assimilation, including variational methods such as 3- and 4-dimensional Variational (3D-Var and 4D-Var) assimila-

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tion (e.g., Elbern and Schmidt, 2001; Chai et al., 2007; Errera et al., 2008; Hooghiemstra et al., 2011), Kalman Filters (e.g., Khattatov et al., 2000; Parrington et al., 2008 and 2009) and Ensemble Kalman Filters (e.g. Arellano et al., 2007; Miyazaki et al., 2012a; Gaubert et al., 2014). Geer et al. (2006) compared different ozone analyses constructed using various assimilation techniques. The MACC system uses ECMWF’s 4D-Var assimilation algorithm (Courtier et al., 1994). The variational methods aim to minimize a cost function that measures the difference between the model background field and the observations by adjusting chosen control variables in order to obtain the best possible forecast. Control variables can for example be the initial conditions (as done in the MACC and ECMWF system, e.g., Dragani et al., 2011), but also emission rates (Tanimoto et al., 2008; Miyazaki et al., 2012b) or other chemical parameters such as kinetic rate constants (Barbu et al., 2009). While several of the initial studies concentrated on stratospheric ozone (e.g., Hólm et al., 1999; Khattatov et al., 2000; Eskes et al., 2002, 2003; Dethof and Hólm, 2004) data assimilation code has now also been implemented to assimilate tropospheric atmospheric composition data in both global and regional model systems (Lahoz et al., 2007; Zhang et al., 2012; Miyazaki et al., 2012a). Many studies still concentrate on ozone (e.g., Barré et al., 2014; Emili et al., 2014; Gaubert et al., 2014), but the assimilation of other species, such as CO (Yudin et al., 2004; Tangborn et al., 2009; Klonecki et al., 2012) and NO2 (Wang et al., 2011; Miyazaki et al., 2012b; Silver et al., 2013) is also being tested. Furthermore, some studies looked at the benefits obtained by the combined assimilation of several species (Hanea et al., 2004; Elbern et al., 2007; Miyazaki et al., 2012a). Several of the studies listed above concentrated on case studies or were run for time periods of a few months at the most. As far as we are aware, the MACC system is the only system run routinely every day to provide global forecasts for atmospheric composition while using data assimilation to provide initial conditions for several species. Concerning reactive trace gases, which are the focus of this paper, the initial version of the MACC system (Hollingsworth et al., 2008) used a coupled setup (Flemming et al.,


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2009a) in which the Model for OZone And Related chemical Tracers (MOZART-3; Kinnison et al., 2007; Stein et al., 2009) Chemical Transport Model (CTM) was coupled to ECMWF’s Integrated Forecasting System (IFS) using the Ocean Atmosphere Sea Ice Soil coupling software (OASIS-4; Valcke and Redler, 2006). The main motivation for developing the coupled system was that the IFS data assimilation algorithm could be used for composition and Numerical Weather Prediction assimilation without the need to integrate complex chemistry schemes. However, this model setup was numerically expensive because of the overhead of the coupler and it did not scale well on ECMWF’s supercomputer. Fields had to be interpolated between the IFS and CTM model grids and transport processes were duplicated. Another disadvantage was that the chemical tendencies were unchanged during the one hour coupling intervals which could lead to problems at the day-night boundary for species with a short chemical lifetime. It was therefore decided to implement the chemistry scheme and its solvers directly in the IFS, together with modules for photolysis, wet and dry deposition, as well as emission injection, to create a more efficient model system called the Composition-IFS (C-IFS, Flemming et al., 2014). Of three candidate CTM versions available in MACC, the chemistry scheme of the Tracer Model 5 (TM5, Huijnen et al., 2010a) was implemented first, while C-IFS versions with MOZART and MOdèle de Chimie Atmosphérique à Grande Echelle (MOCAGE) have only become available recently. Therefore, this paper focuses on the analysis of data assimilation studies performed with C-IFS TM5, i.e., the model that is described in Flemming et al. (2014). Flemming et al. (2014) showed that the current version of the on-line C-IFS implementation using the CB05 chemical mechanism performed better in forecast mode in many aspects than the previously used MOZART CTM version. Tropospheric CO biases were smaller in the Northern Hemisphere as were O3 biases in the upper troposphere. The diurnal cycle of surface ozone was also better represented in C-IFS. However, some problems remained with C-IFS, e.g., an overestimation of surface ozone in late summer and autumn. Tropospheric CO was still underestimated, particularly over Europe and North America, with the largest bias in winter and spring (see Stein et al.,


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2014 for a detailed discussion of this issue). CO was also underestimated over African biomass burning areas. Furthermore, tropospheric NO2 was largely underestimated over East Asia during the winter. In this study we will show that by assimilating O3 , CO and NO2 observations into C-IFS the model fields can be improved. This paper describes the C-IFS data assimilation setup and shows results from initial C-IFS assimilation experiments using O3 , CO and NO2 satellite retrievals for the year 2008. The resulting analysis fields are validated against independent observations and compared with global 3-dimensional fields from the MACC reanalysis (Inness et al., 2013) to assess how the C-IFS data assimilation system compares with the MACC coupled system. The paper is structured in the following way. Section 2 describes the C-IFS model and data assimilation system. Section 3 describes the experiment setup and the data used in the assimilation experiments. Section 4 shows results from the data assimilation experiments and validation against independent observations and fields from the MACC reanalysis. Section 5 finishes with conclusions and outlook.

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The current chemistry scheme implemented in C-IFS is a modified version of the Carbon Bond Mechanism 5 (CB05, Yarwood et al., 2005) chemical mechanism implemented in the TM5 CTM (Huijnen et al., 2010a, 2014; Williams et al., 2013). This is a tropospheric chemistry scheme with 54 species and 126 reactions. For stratospheric ozone the chemical tendencies above the tropopause are computed by a parameterisation based on Cariolle and Teyssèdre (2007). Monthly mean dry deposition velocities are currently based on climatological fields from MOCAGE (Michou et al., 2004). The module for wet deposition is based on the Harvard wet deposition scheme (Jacob et al., 2000; Liu et al., 2001).

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This C-IFS system, called C-IFS (CB05) for the remainder of this paper, has been documented and extensively tested in forecast mode (Flemming et al., 2014; Huijnen et al., 2014). It has also run routinely as a CTM without data assimilation since November 2012 producing daily 5 day forecasts. The anthropogenic emissions used in the C-IFS runs described in this paper come from the MACCity emission data base (Granier et al., 2011), with increased wintertime road traffic CO emissions over North America and Europe according to an early version of the emission correction described by Stein et al. (2014). Biomass burning emissions are provided by MACC’s Global Fire Assimilation System (GFAS v1.0, Kaiser et al., 2012), and biogenic emissions are taken from the POET database for the year 2000 (Granier et al., 2005; Olivier et al., 2003), with isoprene emissions from MEGAN2.1, again for the year 2000 (Guenther et al., 2006). The emissions are injected at the surface and distributed over the boundary layer by the model’s convection and vertical diffusion scheme.

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C-IFS data assimilation system

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The chemical species O3 , CO, NO2 , SO2 and HCHO are incorporated into the ECMWF 4D-Var analysis as additional model variables and can be minimized together with the meteorological ECMWF control variables. O3 , CO, and NO2 are actively assimilated in the model runs described in this paper, i.e., they influence the initial conditions for these species, whereas SO2 and HCHO are only monitored passively and not discussed any further in this paper. SO2 data are only assimilated in the MACC system for volcanic eruptions (e.g., Flemming and Inness, 2013) and HCHO retrievals have large errors and are only used for monthly mean evaluation. At present, the background errors for the chemical species are univariate in order to minimize the feedback effects of the chemical fields on the meteorological variables. Although Miyazaki et al. (2012a) have shown the benefit of including correlations between the background errors of different chemical species, this is not yet included in the C-IFS system. Hence, each compound is assimilated independently from the others. 4272

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In the ECMWF data assimilation system the background error covariance matrix is given in a wavelet formulation (Fisher, 2004, 2006). This allows both spatial and spectral variations of the horizontal and vertical background error covariances. The MACC background errors are constant in time. The background errors for O3 and NO2 used in the C-IFS experiments are based on the ones used in the coupled MACC system (see Inness et al., 2009, 2013), while the background errors for CO are newly calculated for the C-IFS data assimilation runs from an ensemble of C-IFS forecast runs that contained 10 members with perturbations to the model physics, observations, sea surface temperatures and emissions. Differences between pairs of background fields were calculated which have the statistical characteristics of the background errors. The vertical correlations of the O3 and CO background errors were restricted to 5 model levels below and above a level to decouple the lower troposphere from the upper troposphere and stratosphere. In the MACC system a logarithmic control variable is used for NO2 , because if the analysis were based on a linear mixing ratio scale it would be prone to large extrapolation errors, due to the high variability of NO2 in space and time and the difficulties in modelling the error covariances. The NO2 background errors were designed to be practically zero in the stratosphere, because only tropospheric NO2 columns are assimilated in this study and the influence of the assimilation is designed to be limited to the troposphere. Profiles of the Profiles of the standard deviation (SD) of the background errors for CO, O3 and NO2 are shown in Fig. 1. The observation error and background error covariance matrices determine the relative weight given to the observations and the background in the analysis. The C-IFS observation error covariance matrix is diagonal, i.e., the observation errors are assumed to be uncorrelated in the vertical and horizontal. For the chemical observations, observation error values given by the data providers are used. A minimum value of 5 % is taken into account for representativeness errors that are smaller. Observation operators are needed to calculate the model equivalent of the assimilated observations, i.e., of satellite retrievals of the atmospheric composition. The O3 , CO and NO2 observations used in the IFS are total or partial column data, i.e., inte-


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grated layers bounded by a top and a bottom pressure. The model’s background values are either calculated as a simple vertical integral between the top and the bottom pressure levels or by using averaging kernels if these are provided in the data to give the partial or total columns at the time and location of the observations (see also Inness et al., 2013). More information about the assimilated data sets is given in Sect. 3.2 below.

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Table 1 shows the atmospheric composition retrievals for CO, O3 and NO2 that were assimilated in CIFS-AN. Averaging kernels were used for the calculation of the model’s 4274

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To test C-IFS (CB05) in data assimilation mode two experiments were run for the year 2008: an assimilation run (CIFS-AN) in which O3 , CO and NO2 satellite retrievals (see Table 1) were assimilated in addition to the available meteorological data, and a control run (CIFS-CTRL) in which only the meteorological data were assimilated. The underlying C-IFS (CB05) model is identical to the setup described in Flemming et al. (2014) apart from the anthropogenic emissions which were the original MACCity emissions in their runs, i.e. with no adjustment of CO emissions. Both experiments were initialized with data from a C-IFS forecast for 31 December 2007, run at a horizontal resolution of about 80 km (T255 horizontal truncation), and had 60 model levels between the surface and 0.1 hPa. All observations were assimilated in 12 h assimilation windows (9z-21z, 21z-9z), in which two minimizations were run at T95 and T159 corresponding to horizontal resolutions of about 210 and 120 km, respectively. The experiments used IFS model cycle CY40R1, see documentation at http://nwmstest.ecmwf.int/research/ ifsdocs/CY40r1/ and https://software.ecmwf.int/wiki/display/IFS/Operational+changes.

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The two experiments CIFS-AN and CIFS-CTRL, as well as fields from the MACC reanalysis (REAN, Inness et al., 2013), are compared against each other and independent observations that were not used in either CIFS-AN and REAN. Initial validation results from REAN are shown in Inness et al. (2013) and more detailed validation can be found in the MACC reanalysis validation reports available from http://www.copernicus-atmosphere.eu/services/aqac/global_ 4275

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first-guess fields in the observation operators where available, i.e., for CO data (Thermal Infrared retrieval product) from the Measurements of Pollution in the Troposphere (MOPITT) instrument and NO2 data from Ozone Monitoring Instrument (OMI). The satellite retrievals of atmospheric composition were thinned to a horizontal resolution ◦ ◦ of 1 × 1 by randomly selecting an observation in the grid box to avoid oversampling and correlated observation errors. Background quality checks and Variational quality control (Andersson and Järvinen, 1999) were applied to all atmospheric composition data. The background quality check rejected observations if the square of the normalized background departure was greater than 5, while the variational quality control reduced the weight of observations that had large departures but still passed the firstguess check. Data flagged as “bad” by the data providers were discarded. Variational bias correction (Dee and Uppala, 2009) was applied to ozone column data from the OMI and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMCHY), while the partial column Solar Backscatter ULTa-Violet (SBUV/2), and profile Microwave Limb Sounder (MLS) and Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) data were used to anchor the bias correction, i.e., they were assimilated without bias correction. Experience from the MACC reanalysis has shown that it is important to have an anchor for the bias correction, to avoid drifts in the fields (Inness et al., 2013). For CO and NO2 data no bias correction was applied in CIFS-AN because data from only one instrument were assimilated and it was not possible to anchor the variational bias correction.


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verification/validation_reports/. It should be noted that the configurations of REAN and CIFS-AN are different because the underlying chemical model and some of the assimilated datasets have changed (see Table S1 and also Inness et al., 2013). Several of these differences (for example differences in the chemical mechanisms, the biomass burning emissions, the dry deposition velocity fields, assimilated data and an enhancement factor for traffic CO emissions in C-IFS) are likely to have an impact in the lower troposphere, where the sensitivity of the assimilated satellite data is low. Nevertheless, it is useful to compare CIFS-AN with REAN because REAN is a documented and widely used dataset produced with the coupled MACC system that can serve as a benchmark for the validation of CIFS-AN. Table 2 lists the datasets used in this paper for the validation of CO, O3 and NO2 fields. More detailed information about the validation datasets can be found in the Supplement.

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This section presents results from the C-IFS experiments highlighting the impact of the assimilation of satellite data on the CO, O3 and NO2 fields in CIFS-AN. 4.1 4.1.1

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As a first step, the impact of the assimilation of MOPITT total column CO (TCCO) data in CIFS-AN is evaluated by looking at the distribution of analysis departures (i.e., observation minus analysis (obs-an) values) in the form of histograms from CIFS-AN and CIFS-CTRL for 2008, for all MOPITT data that were flagged as good quality by the data producers (Fig. 2). Note that in CIFS-CTRL the MOPITT TCCO data were included passively in the analysis, so that the departures statistics could be calculated. Figure 2 shows that the analysis is drawing to the MOPITT data and the biases with respect to 4276

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MOPITT are more than halved in all regions compared to CIFS-CTRL. The values of the annual mean departures (listed in Fig. 2), their root mean square (RMS) and SD for the NH, Tropics and SH show that there are reductions in all these diagnostics in all areas. The plots also show that CIFS-CTRL underestimates CO in the NH compared to MOPITT (obs-an > 0) and overestimates CO (obs-an < 0) in the Tropics and SH. This is in agreement with what was found for C-IFS (CB05) forecast runs by Flemming et al. (2014). The seasonal mean TCCO analysis increments (analysis minus forecast values) and a timeseries of zonal mean TCCO analysis increments are shown in the Supplement (Figs. S1 and S2). They illustrate where the assimilation reduces or increases the TCCO field. It should be noted that after a large initial correction (Fig. S2) the TCCO increments are small: less than 1 % in the zonal mean and less than 4 % in the seasonal means. This illustrates that the analysis is drawing to the TCCO data and that the information brought into the analysis by the data is maintained and carried over into the subsequent analysis cycles. Figure 3 shows zonal mean timeseries of MOPITT TCCO data which are used in CIFS-AN between 65◦ N and 65◦ S, MOPITT analysis departures from CIFS-AN and CIFS-CTRL, and differences between the experiments. The analysis departures are small in CIFS-AN, while they show an overestimation in CIFS-CTRL in the NH and an underestimation in the Tropics and SH (as already noted in Fig. 2). The assimilation increases TCCO at high northern latitudes in winter and spring, when the CO lifetime is longest, and reduces it in the Tropics throughout the year. This is also confirmed in Fig. 4 which shows the seasonal mean vertical differences between CIFS-AN and CIFS-CTRL. In all seasons, CO is reduced in the tropics throughout the troposphere and in the mid and upper troposphere in the SH. It is also reduced in the upper troposphere of the NH in March, April, May (MAM), June, July, August (JJA) and September, October, November (SON). CO is increased below 400 hPa in the NH extratropics in January and February (JF), MAM and SON and in the SH in MAM, JJA and SON, with


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Figure 5 shows timeseries of monthly mean CO from MOZAIC aircraft data and the three experiments averaged over the lower troposphere (LT, 1000–700 hPa), the mid troposphere (MT, 700–400 hPa) and the upper troposphere (UT, 400–200 hPa) near Frankfurt and Windhoek airport. At Frankfurt, which has the largest number of profiles per month of all MOZAIC airports, all experiments manage to reproduce the seasonal cycle seen in the observations with highest CO values at the end of northern spring due to the longer lifetime of CO and higher anthropogenic emissions during winter and spring. CIFS-CTRL underestimates CO in the LT and MT throughout the year with the largest bias of between 20–40 ppb in LT during the winter months, when CO concentrations are highest. In UT CIFS-CTRL overestimates CO. This was also noticed in the stand-alone C-IFS runs described by Flemming et al. (2014). The assimilation of MOPITT TCCO data improves the fit to the MOZAIC data by increasing CO in LT and MT and reducing it in UT during the winter and spring months. This change agrees with the zonal mean differences seen between CIFS-AN and CIFS-CTRL in Fig. 4 and illustrates that assimilating total column CO data can help to improve the vertical structure of the CO field by applying a 4D-Var technique. Between June and October, when the model performs better, the differences between CIFS-AN and CIFS-CTRL are small. Compared to REAN, which was created with an earlier version of the MACC system, CIFS-AN has an improved fit to the MOZAIC data in LT throughout the year, with particular improvements during winter and spring. This can partly be attributed to differences in the traffic emissions used in the runs. Stein et al. (2014) showed that the increased anthropogenic traffic emissions used in CIFS-AN had a large and positive effect on modelled NH CO concentrations. However, even when using the same anthropogenic emissions (as done in Flemming et al., 2014, their Fig. 8) the MOZART-CTM, which was coupled to IFS in REAN, has lower CO values at Frankfurt than a C-IFS (CB05) 4278

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the largest increases in the boundary layer. In JJA the biggest increase in the NH is seen around 400 hPa, where MOPITT has the largest sensitivity.


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stand alone run. Hence differences between the MOZART and C-IFS (CB05) physics and chemistry (e.g., different OH distributions and different parameterizations of dry deposition) also contribute. REAN agrees better with the MOZAIC data in MT during summer which is likely to be due to the assimilation of additional IASI TCCO data in REAN. At Windhoek all experiments underestimate the September/October maximum due to biomass burning in LT and MT, but the assimilation of TCCO data leads to increased CO values in CIFS-AN and REAN and therefore smaller negative biases than CIFSCTRL which underestimates the peak by 40–50 ppb, possibly due to an underestimation in the GFAS CO emissions. At other times of the year the impact of the assimilation in LT and MRT is smaller, and CIFS-AN has slightly lower CO values in LT and MT than CIFS-CTRL, which improves the fit to the MOZAIC data during some months and degrades it during others. The largest impact of the assimilation from January to September can be seen in UT where CIFS-AN is about 10 ppb lower than CIFS-CTRL. This is in agreement with the zonal mean differences seen in Fig. 4. Here, the fit to the MOZAIC data is degraded in CIFS-AN from January to April, but improved during the summer. CIFS-AN and REAN are of similar quality at Windhoek. REAN has a better fit to the MOZAIC data during in LT and MRT during the biomass burning season, but a larger negative bias than CIFS-AN in UT. CO from the C-IFS experiments is further validated against NDACC FTIR data for timeseries of tropospheric CO columns (from the surface to 10 km), as well as annually averaged CO and bias profiles (Fig. 6). All experiments underestimate the tropospheric CO columns at the northern FTIR stations with annual mean biases at Eureka of −6.0, −7.3, −16.9 % and at Jungfraujoch of −3.5, −3.5 and −3.0 % for CIFS-AN, CIFS-CTRL and REAN, respectively. At Eureka, the largest difference between CIFS-AN and CIFSCTRL are seen during winter. This agrees with the TCCO differences seen in Fig. 3. As already seen in Fig. 4, in the NH the assimilation of MOPITT TCCO leads to increased CO values in the mid and lower troposphere and to reduced CO values in the upper troposphere. This improves the fit to the FTIR data in CIFS-AN at Jungfraujoch and at


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Eureka in the lower and mid troposphere, but leads to a worse fit than CIFS-CTRL in the upper troposphere at Eureka. REAN has a larger negative bias at Eureka after April. In Inness et al. (2013) and MACC Reanalysis validation reports (available from www. copernicus-atmosphere.eu) it was noted that the assimilation of IASI TCCO retrievals that started in REAN in April 2008 led to lower surface CO values in Polar regions. While this improved the fit to surface observations over the Antarctic it led to a larger negative bias at Arctic stations (see also GAW validation below). At Izaña all experiments overestimate CO below 500–600 hPa, and underestimate it above, with the largest biases in REAN. The differences between CIFS-AN and CIFSCTRL are small, which can also be seen in the annual mean tropospheric column biases of −6.6 % for CISF-AN and −7.5 % for CIFS-CTRL. At the SH station of Lauder all models underestimate CO below 700 hPa and overestimate it above, with the largest positive bias in CIFS-CTRL and lowest in REAN. This large bias in CIFS-CTRL can also be seen in the Lauder FTIR timeseries of tropospheric CO. Figure 6 illustrates that the large reduction in upper tropospheric CO values due to the assimilation of MOPITT TCCO (seen in Fig. 4) leads to an improved fit with the FTIR data in this region. Figure 7 shows an evaluation of monthly mean surface CO volume mixing ratios from the experiments against a selection of GAW stations. As already seen in the difference plots in Fig. 4 and the MOZAIC LT comparison in Fig. 5 the differences between CIFSAN and CIFS-CTRL in the NH are largest during the winter season, when the CO lifetime is longest and the assimilation of MOPITT TCCO leads to increased surface CO values. The seasonal cycle is very well captured by CIFS-AN at Alert with a negligible annual mean bias, while CIFS-CTRL has a bias of −7 parts per billion volumn (ppbv). At Mace Head there is again good agreement of CIFS-AN with the observations with a mean bias of 4 pbbv, compared to −6 ppbv in CIFS-CTRL. At both stations REAN has a larger negative bias (−30 and −8 ppbv, respectively). This is in agreement with the large negative bias of REAN relative to FTIR data at Eureka (Fig. 6) and due to the assimilation of IASI TCCO retrievals that started in REAN in April 2008 and led to lower surface CO values in Polar regions. At Key Biscayne all 3 experiments agree well with


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the observations, and REAN has the smallest annual mean bias. At Ascension Island the experiments capture well the change from low CO surface concentrations between January to June, to higher values from August onwards, which are related to transport of CO rich air from the African biomass burning areas. REAN overestimates CO during the second half of the year and has the largest annual mean bias (7 ppbv). CO values are lower in CIFS-AN than in CIFS-CTRL (−3 and 2 ppbv mean bias, respectively), but mainly within the SD of the observations. The lower values in CIFS-AN than in CIFS-CTRL between January and July agree with what is seen in comparison with MOZAIC data at Windhoek in LT (Fig. 5). At Samoa all 3 experiments capture the low CO background values over the Pacific, but CIFS-CTRL overestimates CO more throughout the year (mean bias of 4 ppbv), while CIFS-AN underestimates it in the first half of the year and overestimates it in the second half, leading to an annual mean bias of 0 ppbv. At South Pole CIFS-AN and CIFS-CTRL overestimate the surface CO values with larger biases in CIFS-AN than in CIFS-CTRL (9 and 7 ppbv, respectively). In REAN the agreement with the observations is noticeably improved after the start of the assimilation of IASI CO in April 2008. The comparisons with independent validation data have shown that by assimilating total column CO retrievals several aspects of the three-dimensional CO field can be improved compared to a control run without data assimilation. In the NH, the largest impact is an increase of CO in the lower troposphere and at the surface during NH winter and spring. In the Tropics CO is decreased throughout the troposphere, and in the SH CO is decreased in the mid to upper troposphere. It may be possible to further improve the vertical structure of the CO field by assimilating retrieved CO profiles from MOPITT, IASI or TES instead of the total column products. The C-IFS (CB05) model has problems capturing the summer-time CO maximum due to biomass burning at Windhoek in the SH, and the assimilation can only partly correct this. Here it might be beneficial to have improved biomass buring emissions that use a more realistic injection height. Also C-IFS (CB05) overestimates CO production originating mostly from isoprene emissions and chemistry over Indonesia and Central Africa (see Fig. S1).


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The histograms of SCIAMACHY and OMI analysis departures in Fig. 8 illustrate that CIFS-CTRL has large TCO3 biases and that the assimilation of ozone retrievals is essential to improve the fit with the OMI and SCIAMACHY data. The signs of the biases are consistent for SCIAMACHY and OMI. TCO3 is dominated by ozone in the stratosphere and having a simple photochemical parametrisation of the stratospheric ozone chemistry (see Sect. 2.1) is clearly a weakness of C-IFS (CB05). CIFS-CTRL overestimates TCO3 in the NH (obs-an < 0) with a mean annual bias of 22 Dobson Units (DU) relative to SCIAMACHY and 14 DU relative to OMI. It underestimates TCO3 in the Tropics by −18 DU relative to SCIAMACHY and −28 DU relative to OMI in the annual mean, and in the SH by −7 DU relative to SCIAMACHY and −19 DU relative to OMI. Figure 9 shows that, as expected, the fit to MLS and MIPAS profile data is also strongly improved and that the assimilation of ozone retrievals leads to much smaller biases and SDs of the departures in the vertical in CIFS-AN. Figure 10 shows the zonal mean TCO3 differences of the experiments and the assimilated OMI observations, and illustrates how the assimilation leads to lower O3 values in the Extratropics and higher values in the Tropics and to a much improved fit with the OMI data compared to CIFS-CTRL. The seasonal mean vertical differences between CIFS-AN and CIFS-CTRL are given in Fig. 11 and show large differences between the two experiments. Seasonal mean OMI analysis increments and a timeseries of the zonal mean analysis increments are shown in the Supplement (Figs. S4 and S5). Like for CO, the analysis increments are small (mainly less than 1 %) after an initial adjustment in January 2008.

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Timeseries of monthly mean tropospheric O3 from ozone sondes and the experiments averaged over the LT, MT and UT are shown in Fig. 14 for Europe, North America and East Asia and in Fig. 15 for Tropics, Arctic and Antarctic. It should be stressed that only ozone total column and stratospheric profile ozone data (see Table 1) are assimilated

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Figure 12 shows timeseries of the monthly mean TCO3 from the experiments and KNMI’s Multi Sensor Reanalysis (MSR) for the year 2008 for the NH, Tropics and SH. The figure confirms that the assimilation of ozone retrievals leads to a greatly improved TCO3 in CIFS-AN compared to CIFS-CTRL which overestimates TCO3 with respect to the MSR data in the NH by up to 40 DU, and underestimates it in the Tropics (up to −50 DU) and to a smaller extent in the SH (up to −30 DU, but good agreement of the columns from Aril to July). Despite the simple stratospheric ozone parameterization (see Sect. 2.1) used in C-IFS (CB05), CIFS-AN shows better agreement with the MSR data than REAN, illustrating the strong constraints of the assimilation of ozone data for providing good quality total column fields. Figure 13 shows timeseries of monthly mean stratospheric O3 biases between the experiments and ACE-FTS and MIPAS data for stratospheric layer between 30–70 hPa for the Antarctic, Tropics and Arctic. Plots for the layers 10–30 and 70–150 hPa are shown in Figs. S5 and S6 in the Supplement. The figures show that in all three altitude ranges the assimilation leads to an improved fit to the ACE and MIPAS data and that biases and SDs are much reduced in CIFS-AN compared to CIFS-CTRL. The biases of CIFS-AN with respect to ACE-FTS are never larger than 15 %. The assimilation corrects especially well the large biases modeled by CIFS-CTRL above the Antarctic. Lefever et al. (2014) showed that this success is primarily due to the assimilation of profile data, such as MLS or MIPAS. The differences between CIFS-AN and REAN are small in all areas and altitude ranges.

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in CIFS-AN and REAN and that the impact on the troposphere comes as the residual of combining those datasets. The seasonal cycles are well reproduced in all experiments in most areas, but there are some biases compared to the sonde data, particularly in LT and for CIFS-CTRL also in UT. In all 6 areas, O3 in the UT is improved in CIFS-AN compared to CIFS-CTRL as the impact of the assimilation of stratospheric and total column ozone data corrects model biases here. CIFS-AN and REAN are generally very close in the UT, except in the Tropics where CIFS-AN fits the observations better. Note that the tropopause is higher in the Tropics and that O3 in UT is more influenced by the modelling of tropospheric processes, and hence differences in the chemistry schemes, than at higher latitudes. In MT and LT the differences between CIFS-AN and CIFS-CTRL are smaller than in UT, but there are larger differences between CIFS-AN and REAN here. This indicates that the impact of the assimilated data gets smaller and the differences between the chemistry schemes become more important lower in the troposphere. In LT the spring and summer time O3 maxima over Europe and North America are overestimated by CIFS-CTRL and this overestimation is not corrected in CIFS-AN. However, during winter and spring the assimilation has some impact on LT, and CIFS-AN agrees better with the observations over Europe and North America than CIFS-CTRL. REAN also overestimates O3 in LT over Europe during the summer, but less so over North America. In MT CIFS-AN has the best fit to the observations over Europe, but a worse fit than CIFS-CTRL over North America. Over East-Asia (the average of Hong-Kong and three Japanese stations, see Table S5) O3 in LT is overestimated throughout the year with little differences between CIFS-CTRL and CIFS-AN, apart for smaller biases in CIFS-AN from October to December. REAN also overestimates O3 in LT but has the best fit to the observations from March to May. In MT the assimilation leads to an improved fit with the sondes over East Asia during winter. At other times of the year CIFS-AN and CIFS-CTRL are similar and agree better with the sondes than REAN. The O3 timeseries in the Tropics (Fig. 15) is characterized by two ozone maxima due to biomass burning during the dry seasons in South America (Arpil/May) and Indonesia


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(September). CIFS-CTRL can not reproduce these peaks well in the MT and UT and the assimilation improves the fit to the sondes, particularly in UT and to a smaller extent in MRT. In LT CIFS-AN has a larger positive bias than CIFS-CTRL. CIFS-CTRL also had problems capturing the high CO values see at Windhoek during the biomass burning season (see Fig. 5) and the lower O3 values might be a result of an underestimation of the O3 production because of an underestimation of the precursors. In the Arctic the seasonal cycle with maximum in late spring is well reproduced in all experiments, but there are some biases. In LT CIFS-CTRL overestimates the observed O3 while CIFS-AN and REAN underestimate O3 . In the MT CIFS-CTRL has the best agreement with the observations while CIFS-AN has a negative bias. CIFS-AN and REAN agrees best with the observations in UT. In the Antarctic CIFS-AN and CIFS-CTRL underestimate O3 in LT and MT but roughly capture the seasonal cycle, while REAN clearly has problems reproducing the ozone distribution in LT and MT. This is due to vertical correlation in the background error statistics used in REAN. REAN performed so badly in the Polar lower troposphere because large biases in stratospheric ozone in the underlying model in Polar regions (see Inness et al., 2013) required large corrections by the analysis. The background errors used in REAN had vertical correlations between the lower troposphere and the upper troposphere and stratosphere which led to poor vertical tropospheric O3 profiles over the poles as the assimilation of stratospheric data led to (unwanted) changes near the surface. The ozone background errors were modified for CIFS-AN (see Sect. 2.2) to remove these correlations, and CIFS-AN scores better here. In all runs NO2 is underestimated over areas of anthropogenic pollution (see Figs. 22 and 23 below), which is a well known problem in the MACC system (Inness et al., 2013; Flemming et al., 2014). The model is not able to resolve local-scale high levels of NOx observed in polluted areas because of its coarse resolution, but distributes this over the whole gridbox. Therefore, with more diluted NO2 in high pollution regions, the model is shifted towards a regime of O3 production (NOx -limited) rather than O3 loss, which might contribute to the positive O3 bias seen in LT in all areas except Antarctica. Such


The histograms of OMI analysis and first-guess departures in Fig. 17 illustrate that the reductions of bias, RMS and SD due to the assimilation of OMI tropospheric NO2 column (TRCNO2) retrievals are much smaller than the impact seen from the assimilation of CO and O3 data (Figs. 2 and 8) and the distributions remain skewed towards positive departures (observations > analysis). This does not mean that the assimilation of NO2 has no impact in the model. Figure 18 shows the seasonal mean NO2 analysis

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high bias of O3 in the LT at northern mid-latitudes is a general problem of global-scale CTMs, e.g., Young et al. (2013). Figure 16 shows modified normalized mean biases (MNMBs) and correlation coefficients from the 3 experiments against GAW stations (see Table S4) for 2008. CIFSCTRL has a positive bias at the surface, except over Antarctica, as already seen in Figs. 14 and 15. The assimilation generally leads to lower surface O3 and reduces the MNMB in the Arctic and NH Midlatitudes, but the differences are small. The correlations are not changed noticeably in CIFS-AN. REAN has larger negative biases than the C-IFS runs in the Polar Regions and in Midlatitudes. The differences between REAN and CIFS-AN are particularly large in the Polar Regions due to the background error formulation used in REAN as already discussed above. In summary, comparing the experiments with tropospheric ozone observations shows that there is some positive impact on the troposphere, even though only O3 total column and stratospheric profile data (see Table 1) were assimilated. The improvement is particularly large in the UT, but smaller in MT and LT where characteristics of the underlying chemistry scheme become more important. There are, however, some pronounced improvements in CIFS-AN compared to REAN in LT and surface ozone, which are at least partly the result of modifications to the ozone background error correlations used in CIFS-AN.


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column increments from CIFS-AN and illustrates that the NO2 increments are considerably larger (> 20 % over most land surfaces) than the average increments for CO and O3 (see Figs. S1 and S3). These large NO2 analysis increments can further be seen in the zonal mean time series (Fig. 19). Figure 19 also illustrates that, unlike the TCCO and TCO3 increments, there is no initial adjustment followed by smaller analysis increments, but that the increments remain of similar magnitude throughout 2008. For CO and O3 the analysis is drawing to the assimilated data and the information is maintained and carried over into the next analysis cycles, because of the longer lifetimes of these species. The background field for a subsequent analysis cycle is therefore closer to the data, and the analysis increments get smaller with time. Because of the short lifetime of NO2 , however, the information brought into the analysis by the OMI NO2 data is quickly lost and not carried over into the next analysis cycle. This is further illustrated in Fig. 20 which compares seasonal mean differences between the NO2 analysis fields from CIFS-AN and CIFS-CTRL and differences of 12 h forecasts started from these analyses. While there are large differences between the CIFS-AN and CIFS-CTRL NO2 analyses, these differences are almost entirely lost in the subsequent 12 h forecast. The largest remaining differences between the forecasts are seen in JF in the NH when the NO2 lifetime is longest. This means that with the 12h 4D-Var configuration used in CIFS-AN, most of the information brought into the analysis by OMI TRCNO2 is lost in the subsequent 12 h long trajectory. This is made worse by the fact that OMI NO2 observations are only available during the day, when NO2 is photolysed by sunlight, and observations are only available for part of the globe during every analysis cycle. As noted by Carmichael et al. (2007), Wang et al. (2008) and Silver et al. (2013) perturbations of the initial conditions can be brief for short lived species, as forcing from sources and sinks such as chemistry and emissions will drive the fields back to chemical equilibrium. This limits the usefulness of data assimilation in adjusting the initial conditions for species such as NO2 . Wang et al. (2008) found a small improvement in surface NO2 concentrations when they assimilated OMI NO2 retrievals over Europe, and also some improvement in the next day forecast. They concluded that the impact


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The validation with GOME-2 TRCNO2 data in Fig. 21 confirms that, in absolute terms, the differences between CIFS-AN and CIFS-CTRL are small. Figure 21 shows maps of annual mean TRCNO2 from GOME-2 and the three experiments. The experiments capture the global NO2 distribution seen by GOME-2 well with high values over areas of high anthropogenic, as well as boreal and tropical biomass burning emissions. This illustrates that C-IFS (CB05) and the coupled MACC system that was used in REAN have a reasonable NO2 field despite the limited impact of the NO2 assimilation (Fig. 17). However, there are some noticeable differences between the modelling experiments and the GOME-2 retrievals. The experiments underestimate TRCNO2 over the regions of anthropogenic pollution in Europe, North-America and East Asia and also the tropospheric background values over Africa, Eurasia and Australia. Furthermore, the models overestimate satellite values over India, the Persian Gulf and the Red Sea, and ship tracks (e.g., over the Indian Ocean) are more pronounced in the experiments than in the GOME-2 columns. The ship plumes are highly concentrated just after release, and fast initial chemistry is not described in the course resolution model with instantaneous chemistry. On the other side, ship emission inventories are also very uncertain and may be too high (Vinken et al., 2014). Comparison of the experiments against area averaged timeseries of monthly mean GOME-2 TRCNO2 shows that magnitude and seasonality of tropospheric NO2 columns (Fig. 22) over Europe and North-America are rather well reproduced, indicating that emission patterns and NOx photochemistry are reasonably represented here. However, all experiments tend to underestimate satellite values over Europe during the summer, but might be within the error bars of the retrieval which can have large uncer4288

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might vary with season because of the shorter lifetime of NO2 during the summer. This agrees with our Fig. 20. Our study confirms that short lived species like NO2 would be more successfully corrected by adjusting the emissions instead of the initial conditions (e.g., Elbern et al., 2000; Miyazaki et al., 2012a).


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tainties (e.g., van Noije et al., 2006). This low bias against satellite data was also seen for other regional models (Huijnen et al., 2010b). The simulations significantly underestimate the annual cycle of NO2 columns over East-Asia, where the wintertime maximum is severely underestimated while the summertime values agree better. Part of this might be due to an overestimation of TRCNO2 by the GOME-2 retrieval, which gives higher values here during winter than other retrieval algorithms (van Noije, 2006). Further reasons could be an underestimation of anthropogenic NO2 emissions, too short lifetime of simulated NO2 , and uncertainties in the chemistry. It will have to be investigated how important factors like injection height, diurnal cycle of the emissions and the horizontal model resolution are for correctly modelling the TRCNO2 values in this area. All runs tend to exaggerate the annual cycle for South Africa, where they overestimate NO2 during the biomass burning season. This was already noted for REAN in Inness et al. (2013) and seems to be related to too large NOx emission factors used in GFAS. The differences between CIFS-AN and CIFS-CTRL are small. The largest differences are seen over the Eastern US where CIFS-AN has higher NO2 values than CIFS-CTRL with leads to smaller biases from February to July, and larger biases during the rest of the year. Figure 23 evaluates NO2 profiles from CIFS-AN and CIFS-CTRL against MAX-DOAS measurements over Beijing from the surface to 3.5 km. The MAX-DOAS instrument is located in the Beijing city centre close the Olympic Stadium and the horizontal extent of the measurements varies between a few and a few dozen of km, depending on the pollution, so the representative error of the model relative to the measurements is bound to be large. Both experiments have a negative NO2 bias, but there is a clear difference between the experiments and a smaller bias in CIFS-AN. The mean bias of the partial column is reduced from −22 % in CIFS-CTRL to −14 % in CIFS-AN. These values are larger than the mean relative uncertainty for all measurements which is 12 % (Hendrick et al., 2014). The timeseries of the NO2 columns shows that when there is quite homogeneous urban pollution, e.g., between the end of June and the middle of August, the model fits the observations well. This is not the case in autumn/winter when there


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are numerous strong local pollution events. Now the pollution background is still well captured by the model but the high NO2 peaks are not (despite some improvements in CIFS-AN). This agrees with the larger underestimation seen in East Asia relative to GOME-2 during winter. Conclusion and future outlook

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A new chemistry transport model, the Composition-IFS (Flemming et al., 2014), was developed as part of the MACC project. This C-IFS model is based on ECMWF’s Integrated Forecasting System and includes modules for chemistry, deposition and emissions of reactive gases. Several of the chemistry variables have been included as control variables in the data assimilation part of the IFS so that initial conditions for these fields can be modified by assimilating observations of atmospheric composition. The performance of C-IFS in data assimilation mode was tested by assimilating satellite retrievals of CO, O3 and NO2 from various sensors (see Table 1) for the year 2008. The results were compared with a control run without data assimilation, with fields from the MACC reanalysis and with independent observations (see Table 2). Assimilating MOPITT TCCO led to an improved total column CO field compared to the control run, and also to some improvements in the vertical distribution of CO and the CO concentrations in the lower troposphere, with the largest impact in the NH winter. In the Tropics there was also some improvement compared to the control run in surface and lower tropospheric CO in the C-IFS analysis, particularly during the South African biomass burning season. The C-IFS analysis captured the seasonal cycle of surface CO better than the MACC reanalysis at several GAW stations. In future work, it will be tested if the assimilation of MOPITT, IASI or TES CO profiles can help to further correct the 3-dimensional distribution of CO. Furthermore, model runs will be carried out to assess if using the latest GFAS v1.2 biomass burning emissions, which use a plume rise model to calculate injection heights, can lead to an improved representation of CO in the lower and mid troposphere during the tropical biomass burning season.

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The simple stratospheric ozone photochemical parameterisation used by the stand alone C-IFS (CB05) system to model the stratospheric ozone field was always designed to be used in a data assimilation context and clearly leads to a very biased stratospheric and total column ozone field in the C-IFS control run. The assimilation of a combination of ozone total column and stratospheric profile retrievals (see Table 1) greatly improves the total column, the stratospheric and the upper tropospheric ozone field in the C-IFS analysis compared to the control run. No tropospheric O3 data were assimilated in our tests. Therefore, the impact on tropospheric O3 came from the residual of total column O3 and the stratospheric profile data and was smaller in the mid and lower troposphere than in the upper troposphere, as characteristics of the chemistry scheme became more important. For example, a large positive bias in lower tropospheric ozone over East-Asia was not reduced by the analysis, and there was little impact on lower tropospheric ozone over Europe and North America during the summer. Nevertheless, there was some improvement in the C-IFS analysis in the troposphere and the positive ozone bias seen in the C-IFS control run over Europe and North America during winter and spring in the lower troposphere was reduced. It is planned to test the assimilation of IASI tropospheric O3 columns or IASI O3 profiles in combination with the MLS and ozone column data, which should allow for a better correction of tropospheric ozone (e.g., Emili et al., 2014; Barré et al., 2014). Despite its simple O3 chemistry parametrisation the C-IFS O3 analysis was of similar quality to the MACC reanalysis which used a more comprehensive stratospheric ozone chemistry scheme. The impact of the assimilation of tropospheric NO2 column retrievals was small because of the short lifetime of NO2 . Even though the assimilation led to large analysis increments this information was not retained by the model, and most of the impact of the data assimilation was lost from one analysis cycle to the next. It might be possible to improve this slightly by using a shorter assimilation window, e.g., 6h 4D-Var, and by using NO2 retrievals from more than one satellite with different overpass times, but ideally the NO2 data should be used to adjust the emissions instead of the initial conditions.


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Compared to GOME-2 TRCNO2 retrievals, C-IFS with and without assimilation of OMI TRCNO2 data, severely underestimated wintertime NO2 over East Asia and overestimated NO2 over Southern Africa during the biomass burning season. At other times and in other regions the agreement was better. An underestimation was also found with respect to MAX-DOAS observations at Beijing. However, in order to increase the statistical significance of the validation effort using MAX-DOAS data, comparisons will be further extended to other stations. Future plans for the development of the C-IFS data assimilation system include to recalculate the background error statistics for all MACC control variables with the latest configuration of the model, to work on the adjustment of emissions instead of the initial conditions, especially for NO2 , and to investigate the impact of cross correlations in the assimilation of different chemical species, and potentially also the impact of the chemical assimilation on the wind field, which has been suppressed so far. In data assimilation mode C-IFS performs similarly well or better than the coupled system used in the MACC reanalysis for CO, O3 and NO2 , especially in the lower troposphere and at the surface. Based on many tests and comparisons the MACC pre-operational NRT system has now been switched to C-IFS (CB05) to provide daily routine global analysis and forecast fields. The reduced numerical cost of C-IFS (Flemming et al., 2014) will allow to run this system at higher resolution which may lead to additional improvements in the forecasted fields. One limitation of the current C-IFS (CB05) system is that it does not contain a comprehensive stratospheric chemistry scheme, and this paper has shown clearly the resulting problems for stratospheric ozone in the C-IFS control run. While a good stratospheric ozone analysis can be obtained by using a simple stratospheric ozone photochemical parametrisation and assimilating ozone observations, other stratospheric species are not available or poorly constrained in C-IFS (CB05). Work is under way to extend the C-IFS (CB05) scheme with the stratospheric chemical mechanism of the Belgian Assimilation System for Chemical ObsErvations (BASCOE) scheme (Errera et al., 2008). This will yield a CTM that can model both the troposphere and the strato-


Andersson, E. and Järvinen, H.: Variational quality control. Q. J. Roy. Meteor. Soc., 125, 697– 722, 1999.

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Acknowledgements. MACC-II was funded by the European Commission under the EU Seventh Research Framework Programme, contract number 283 576. MACC-III was funded by the European Commission under Horizon2020 as a Coordination & Support Action, grant agreement number 633 080. Ground-based data used in this publication were obtained as part of WMO’s Global Atmosphere Watch (GAW) and its contributing Network for the Detection of Atmospheric Composition Change (NDACC), in particular the FTIR CO data from Eureka, Jungfraujoch, Izaña and Lauder, and Southern Hemisphere Additional Ozonesonde programme (SHADOZ). Their consolidated version is available publicly through the NDACC Data Host Facility (http://ndacc.org), SHADOZ archive (http://croc.gsfc.nasa.gov/shadoz/), and World Ozone and ULTaviolet Data Centre (WOUDC, http://woudc.org). The validations of the NDACC FTIR CO and UVVIS DOAS NO2 measurement were performed within the framework of the NORS project (Demonstration Network Of ground-based Remote Sensing observations in support of the Copernicus Atmospheric Service, funded under the European Community’s Seventh Research Framework Programme, grant n◦ 284 421). We acknowledge the contribution of Michel Van Roozendael in setting up the MAX/DOAS instrument in Beijing and in participating to the retrieving of the NO2 data at this new station.

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sphere. Furthermore, the MOZART and MOCAGE chemistry schemes, which have tropospheric and stratospheric chemistry solvers, are also being implemented into the C-IFS, so that in the near future the C-IFS system might be able to provide comprehensive analyses and forecasts of the troposphere and the stratosphere by an ensemble of CTMs.


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Provider

Version

Type

Data usage criteria

Reference

MIPAS

ENVISAT

KIT

CCI, V220

O3 PROF

All data used

MLS OMI

AURA AURA

NASA NASA

V02 V003

O3 PROF O3 TC

All data used Used if SOE > 10◦

SBUV/2 SBUV/2 SBUV/2 SCIAMACHY

NOAA-16 NOAA-17 NOAA-18 ENVISAT

NOAA NOAA NOAA BIRA

V8 V8 V8 CCI, fv0100

O3 O3 O3 O3

Used if SOE > 6 ◦ Used if SOE > 6 ◦ Used if SOE > 6 Used if SOE > 6◦

MOPITT

TERRA

NCAR

V5

CO TC

OMI

AURA

KNMI

V1.1

NO2 TRC

von Clarmann et al. (2003, 2009). Waters et al. (2006) Bhartia et al. (2002); Levelt et al. (2006) Bhartia et al. (1996) Bhartia et al. (1996) Bhartia et al. (1996) Stiller et al. (2012); Van Roozendael et al. (2012) Deeter et al. (2010, 2013) http://www.temis.nl; Wang et al. (2008)

PC PC PC TC

◦

Used if ◦ ◦ 65 S < lat < 65 N Used if SOE > 6◦ and 60◦ S < lat < 60◦ N

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Table 1. Atmospheric composition satellite retrievals that were used in CIFS-AN. PROF denotes profile data, TC total columns, TRC tropospheric columns, PC partial columns, and SOE solar elevation. PC SBUV/2 data consist of 6 layers between the surface and 0.1 hPa.

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Marenco et al. (1998) Nedelec et al. (2003)

NDACC FTIR

CO profiles and tropospheric columns (see Table S2 for list of stations)

GAW Multi Sensor Reanalysis ACE-FTS

Surface CO (see Table S3) and O3 (see Table S4) Total column O3 (TCO3) Stratospheric O3 profiles

Stratospheric O3 profiles

Ozonesondes

O3 profiles

GOME-2 MAX-DOAS at Beijing

Tropospheric NO2 columns (TRCNO2) NO2 profiles

Raspollini et al. (2013)

Komhyr et al. (1995) Steinbrecht et al. (1998) Richter et al. (2011) Hendrick et al. (2014)

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Oltmans and Levy (1994) Novelli and Masarie (2014) Van der A et al. (2010) Dupy et al. (2009)

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MIPAS

Dils et al. (2006) De Laat et al. (2010) Langerock et al. (2014)

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References

Uncertainty: ±5 ppbv Precision: ±5 % Detection limit: 10 ppbv Uncertainty (smoothing uncertainty not included): Trop. columns 5–9 % Individual levels: 10–25 % ±2–5 ppbv (CO) ±1 ppbv (O3) ∼ 1 DU Bias < 5 % (15–45 km) Precision: 12–15 % above 20 km 17–30 % below 20 km 5–10 % (larger near boundaries of retrieval range) −14 to 16 % above 10 hPa 5 % between 200–10 hPa −7 to 17 % below 200 hPa ±20–30 % 12 %

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Uncertainty

CO profiles at Frankfurt (837 profiles) and Windhoek (323 profiles)

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Validated fields

MOZAIC

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Table 2. Summary of validation data sets used in this study. A more comprehensive description of the data sets can be found in the Supplement.

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Data assimilation with C-IFS

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−1

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Figure 1. Background error SD profiles (blue) at 50 N, 10 E for (a) CO in kg kg , (b) O3 in kg kg−1 and (c) log(NO2 ), dimensionless. Also shown in (a) is the profile for the CO background error SD of the original MACC system used in REAN (green).


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Figure 2. Histograms of MOPITT TCCO analysis departures (observation minus analysis) for ◦ CIFS-AN (red) and CIFS-CTRL (black) for 2008 averaged over the NH (90–20 N), the Tropics ◦ ◦ ◦ 18 −2 (20 N–20 S) and the SH (20–90 S) for all good data in 10 molecules cm .


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Figure 3. Timeseries of (a) zonal mean TCCO from the MOPITT data used in CIFS-AN, (b) TCCO analysis departures (observations minus analysis) from CIFS-CTRL and (d) TCCO anal18 −2 ysis departures from CIFS-AN, all in 10 molecules cm . Shown in (c) is the zonal mean relative difference in % of CIFS-AN minus CIFS-CTRL. In (a) red indicates higher values of the field, blue lower values. In (b–d) red indicates positive values, blue negative values.


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Figure 4. Cross sections of the seasonal mean zonal mean CO differences between CIFS-AN minus CIFS_CTRL in ppb for (a) JF, (b) MAM, (c) JJA and (d) SON 2008. Red indicates positive values, blue negative values.


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Figure 5. Timeseries of monthly mean tropospheric CO in ppb over (a) Frankfurt (50◦ N, 8.6◦ E, 837 profiles) and (b) Windhoek (22.5◦ S, 17.5◦ E, 323 profiles) averaged in the pressure bands 1000–700 hPa (bottom), 700–400 hPa (middle) and 400–200 hPa (top) from MOZAIC aircraft data (black), CIFS-AN (red), CIFS-CTRL (blue) and REAN (orange) in 2008.


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Figure 6. Timeseries of daily mean tropospheric CO columns (surface to 10 km) in 1015 molecules cm−2 (left), annual mean CO VMR profiles in ppbv (middle) and annual mean bias (model minus observation) profiles in % (right, with dashed lines for spread) for the year 2008 for 4 GND NDACC stations: (a) Eureka, (b) Jungfraujoch, (c) Izaña and (d) Lauder. CIFSAN is shown in red, CIFS-CTRL in blue and REAN in orange.


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Figure 7. Timeseries for 2008 of monthly mean surface CO volume mixing ratios (ppbv) at a selection of GAW stations (black), CIFS-AN (red), CIFS-CTRL (blue) and REAN (orange): (a) Alert (82.3◦ N, 62.2◦ W), (b) Mace Head (53.2◦ N, 9.5◦ W), (c) Key Biscayne (25.4◦ N, 80.9◦ W), (d) Ascension Island (7.6◦ S, 14.3◦ W), (e) Samoa (13.5◦ S, 171.5◦ W) and (f) South Pole (90◦ S, 0◦ E). Error bars (only shown for the observations) denote the monthly mean variability in the observations. Also given is the annual mean bias of the three experiments.


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Figure 8. Histograms of SCIAMACHY (left) and OMI (right) TCO3 analysis departures in DU for CIFS-AN (red) and CIFS-CTRL (black) for 2008 averaged over the NH (90–20◦ N, top), the Tropics (20◦ N–20◦ S, middle) and the SH (20–90◦ S, bottom) for all good data.


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Figure 9. (a) Annual mean MLS analysis departures and (b) SD of the departures, as well as (c) analysis departures and (d) SD of the departures of MIPAS in DU from CIFS-AN (red) and ◦ ◦ ◦ CIFS-CTRL (black) averaged over the NH (90–20 N, top), the Tropics (20 N–20 S, middle) ◦ and the SH (20–90 S, bottom) for all good data in 2008.


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Figure 10. Timeseries of (a) zonal mean TCO3 in DU from OMI, zonal mean TCO3 analysis departures in % of (b) CIFS-CTRL and (d) CIFS-AN, and (c) of the zonal mean relative difference of CIFS-AN minus CIFS-CTRL. In (a) red indicates higher values of the field, blue lower values. In (b–d) red indicates positive values, blue negative values.


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Figure 11. Cross sections of seasonal mean zonal mean relative O3 differences in % between CIFS-AN minus CIFS_CTRL in ppb for (a) JF, (b) MAM, (c) JJA and (d) SON 2008. Red indicates positive values, blue negative values.


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Figure 12. Timeseries for 2008 of the mean TCO3 of CIFS-AN (red), CIFS-CTRL (blue), REAN (orange) and the multi sensor reanalysis (black) in DU averaged over the (a) NH Extratropics (30–90◦ N), (b) Tropics(30◦ S–30◦ N) and (c) SH Extratropics (90–30◦ S).

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Figure 13. Timeseries for 2008 of monthly mean differences (top) and SDs (bottom) in % of the experiments, ACE data (plus symbols) and MIPAS data (solid) averaged over the pressure ◦ ◦ ◦ range between 30 and 70 hPa, for the Antarctic (90–60 S, left), the Tropics (30 S–30 N, centre) ◦ and the Arctic (60–90 N, right). CIFS-AN is shown in red, CIFS-CTRL in blue, and REAN in orange.


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Figure 14. Timeseries of monthly mean tropospheric O3 in ppb over (a) Europe, (b) NorthAmerica and (c) East Asia averaged in the pressure bands 1000–700 hPa (bottom), 700– 400 hPa (middle) and 400–200 hPa (top) from ozonesondes (black), CIFS-AN (red), CIFSCTRL (blue) and REAN (orange) in 2008.


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Figure 15. Timeseries of monthly mean tropospheric O3 in ppb over (a) Tropics, (b) Arctic and (c) Antarctic averaged in the pressure bands 1000–700 hPa (bottom), 700–400 hPa (middle) and 400–200 hPa (top) from ozonesondes (black), CIFS-AN (red), CIFS-CTRL (blue) and REAN (orange) in 2008.


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Figure 16. (a) Modified normalized mean biases (MNMBs) in % and (b) correlation coefficients (from daily mean values) for GAW stations during the whole of 2008. The stations are ordered by latitude from north to south. For station numbers see Table S4. CIFS-AN is shown in red, CIFS-CTRL in blue and REAN in orange.


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Figure 17. Histograms of OMI TRCNO2 analysis departures for CIFS-AN (red) and CIFS-CTRL ◦ ◦ ◦ (black) for 2008 averaged over the NH (90–20 N, top), the Tropics (20 N–20 S, middle) and the SH (20–90◦ S, bottom) for all good data in 1015 molecules cm−2 .


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Figure 18. OMI TRCNO2 analysis increment (analysis minus forecast) in % from CIFS-AN averaged over (a) JF, (b) MAM, (c) JJA and (d) SON 2008. Red indicates positive values, blue negative values.


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Figure 19. Timeseries of weekly averaged zonal mean OMI TRCNO2 analysis increment (analysis minus forecast) in % for 2008. Red indicates positive values, blue negative values.


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Figure 20. Seasonal mean differences of analysis fields from CIFS-AN minus CIFS-CTRL (left panels) and differences of 12 h forecasts from CIFS-AN minus CIFS-CTRL (right panels) in 1015 molecules cm−2 for (a) JF, (b) MAM, (c) JJA and (d) SON 2008. Red indicates positive values, blue negative values.


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Figure 21. NO2 tropospheric column retrievals for 2008 from (a) GOME-2, (b) CIFS-AN, (c) CIFS-CTRL and (d) REAN in 1015 molecules cm−2 . Red indicates higher values, blue lower values.


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Discussion Paper | Figure 22. Timeseries of area-averaged tropospheric NO2 columns from GOME-2 retrievals (black), CIFS-AN (red), CIFS-CTRL (blue) and REAN (orange) for (a) Europe, (b) East-Asia, (c) Eastern US and (d) South-Africa in 1015 molecules cm−2 .

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Figure 23. (a) Mean NO2 profiles in ppbv from UVVIS DOAS instrument at Beijing (black), CIFS-AN (red) and CIFS-CTRL (blue), (b) mean bias (solid line) and SD (dotted lines) profiles in % for the period 1 June to 31 December 2008, and (c) timeseries of daily mean partial NO2 column for the layer between 0.3 and 3.5 km in 1015 molecules cm−2 .


Data assimilation with C-IFS

Data assimilation with C-IFS

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